li-human

Q: How do I install li-human?

Run npx skills add https://github.com/jakeschincariol/linkedin-agent-skill --skill li-human in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/li-human/ (personal) or .claude/skills/li-human/ (project).

by jakeschincariolMarketing & SEO

Strip the machine fingerprint out of any draft - em dashes, AI slop words, invisible watermark characters - and score it against a five-check detection panel before it goes out. Use whenever text needs to sound human, when the user says humanize, "does this sound like AI", "remove the em dashes", "de-slop this", "will this get flagged", or before any LinkedIn post, comment, reply or DM is shown to the user.

Installs: 245
Last 24h: +14
Trending rank: #7320
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/jakeschincariol/linkedin-agent-skill --skill li-human

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads li-human automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

li-human

Two tools live in this folder and they both actually run. Use them. Do not eyeball this.

python3 humanize.py draft.txt --report        # clean it, show what changed
python3 detect.py draft.txt                    # score it, five checks
python3 detect.py before.txt after.txt         # prove the delta

Both read slop.json, which is the lexicon: 100+ stock words and phrases with plain-English replacements, 17 invisible character classes, 11 typographic substitutions, and 11 structural tells. It is meant to be edited. If the user has a word they always use that the lexicon strips, remove it from the file.

What gets fixed automatically

1. Invisible characters. Zero-width spaces and joiners, word joiners, soft hyphens, byte-order marks, Unicode tag characters, non-breaking and narrow spaces. A keyboard does not produce these. They survive copy-paste, they are invisible in every editor, and they are the single most mechanical thing in generated text. humanize.py deletes every one, including any remaining Unicode format character it does not have a name for.

2. Typography. Em dash to comma, en dash to hyphen, curly quotes to straight, ellipsis to three dots, bullet character to hyphen. The em dash pass is the one that matters: it collapses — to , and then cleans up the double punctuation that leaves behind.

3. The slop lexicon. delve, leverage, robust, seamless, crucial, tapestry, testament to, moreover, "in today's fast-paced world", "let that sink in" and the rest, each swapped for a plain word, with capitalisation preserved and URLs left untouched.

What does NOT get fixed automatically

Structural tells get flagged, not rewritten, because changing the shape of a sentence needs judgement:

"It's not just X, it's Y" and "not only X but also Y"
Rule-of-three triads
Rhetorical one-word question lines: "The result?"
Rocket, fire, bulb, sparkle and dart emoji
Hashtag walls
Reflex engagement bait: "Thoughts?", "Agree?", "Who else?"
Uniform sentence length and uniform bullet length

That list is your job. Rewrite each flagged line by hand, keeping the meaning, then re-run detect.py. This is the part that moves the score from REVIEW to PASS, and it is the part a script cannot do.

The five checks

detect.py scores five signals 0-100, higher is more human:

check	what it measures	machine looks like
BURSTINESS	sentence-length variation	every sentence the same length
SPECIFICITY	numbers, names, concrete markers per 100 words	abstract nouns, no figures
SLOP DENSITY	lexicon hits per 100 words	stock vocabulary
FINGERPRINT	invisible chars, em dashes, curly quotes per 1k chars	typographically perfect
VOICE	contractions, person, structural tells	no contractions, staged reveals

The verdict weights the mean at 60% and the weakest single check at 40%, because a detector only needs one signal to fire. PASS needs an overall of 70+ with no check below 55.

Say this honestly

These are five local heuristics modelled on the signals public detectors key on. They run entirely on the user's machine and nothing is uploaded. They are not GPTZero, Originality, Copyleaks, Winston or Turnitin, they do not call those APIs, and they cannot promise those verdicts. Fixing what they measure does tend to move those numbers, because they are measuring the same underlying things. That is the claim. Do not make a bigger one on the user's behalf, and do not tell a user their text is undetectable.

Order of operations

humanize.py draft.txt -o clean.txt --report
Read the structural flags. Rewrite those lines yourself.
detect.py draft.txt clean.txt to show the before and after.
If the verdict is not PASS, fix the weakest check named in the output and go again. Two rounds is normal. Five means the draft was written by formula, and the fix is a different draft, not more passes.
Show the user the cleaned text and the score. Never the score alone.

FAQ

What does the li-human skill do?

How do I install li-human?

Run npx skills add https://github.com/jakeschincariol/linkedin-agent-skill --skill li-human in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/li-human/ (personal) or .claude/skills/li-human/ (project).

Which AI agents can use li-human?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is li-human safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains li-human?

It is published by jakeschincariol in jakeschincariol/linkedin-agent-skill. It has 245 recorded installs.